9 papers
How Can Reinforcement Learning Achieve Expert-level Placement?
Ruo-Tong Chen, Ke Xue, Chengrui Gao +7
Chip placement is a critical step in physical design. While reinforcement learning (RL)-based methods have recently emerged, their training primarily focuses on wirelength optimiza…
FlowPlace: Flow Matching for Chip Placement
Peng Xie, Ke Xue, Yunqi Shi +6
Chip placement plays an important role in physical design. While generative models like diffusion models offer promising learning-based solutions, current methods have the followin…
Open3DBench: Open-Source Benchmark for 3D-IC Backend Implementation and PPA Evaluation
Yunqi Shi, Chengrui Gao, Wanqi Ren +6
This work introduces Open3DBench, an open-source 3D-IC backend implementation benchmark built upon the OpenROAD-flow-scripts framework, enabling comprehensive evaluation of power,…
ReMaP: Macro Placement by Recursively Prototyping and Packing Tree-based Relocating
Yunqi Shi, Xi Lin, Zhiang Wang +8
This work introduces the ReMaP method, which generates expert-quality macro placements through recursively prototyping and packing tree-based relocating. We first perf…
BBOPlace-Bench: Benchmarking Black-Box Optimization for Chip Placement
Ke Xue, Ruo-Tong Chen, Rong-Xi Tan +5
Chip placement is a vital stage in modern chip design, and black-box optimization (BBO) has been applied to it for decades. Early BBO efforts, however, were limited by immature pro…
Quality-Diversity Red-Teaming: Automated Generation of High-Quality and Diverse Attackers for Large Language Models
Ren-Jian Wang, Ke Xue, Zeyu Qin +7
Ensuring the safety and robustness of large language models (LLMs) is a fundamental challenge and a critical prerequisite for the responsible deployment of artificial intelligence.…